The impact of vision loss on attitudes toward autonomous vehicles: A vision-centric analysis.

SIGNIFICANCE: Autonomous vehicles (AVs) have the promise to be an alternative transportation solution for those with vision loss. However, the impact of vision loss on the perceptions and concerns of AVs is unknown. This study therefore examined whether AVs are perceived differently by blind, visually impaired (VI), and normally sighted people. PURPOSE: This study compared the perceptions of AVs among the blind, VI, and normally sighted. METHODS: Participants' opinions on four perception measures (general opinion, trust, impact on quality of life, and intention to use AVs) and nine concerns regarding AVs were measured. The survey was administered to 51 normally sighted, 68 VI, and 65 blind participants. Analyses of covariance assessed whether the four perception measures and nine concerns varied by vision status (normal vision, VI, blind) and driving status (driver, nondriver). Univariate correlations and multiple regression analyses identified associations and predictors of AV perceptions and concerns from demographic, mood, cognition, travel behavior, and vision measures, which included visual acuity, contrast sensitivity, and visual field. RESULTS: The blind (p<0.001), VI (p<0.001), and nondrivers (p<0.001) showed a greater intention to use AVs compared with those with normal vision and drivers. Similar findings were found for the other perception measures. As visual acuity, contrast sensitivity, and visual field extent declined, positivity toward AVs increased (p<0.001). Visual field extent best predicted general opinion and trust in AVs, whereas driving measures were the best predictors of impact on quality of life and intention to use AVs. Concerns about AVs showed no differences based on vision (p=0.94) or driving (p=0.63) status. CONCLUSIONS: Individuals with vision loss expressed more acceptance of AVs despite their concerns. How positive someone is toward AVs appears to be dependent on their visual field extent and driving status.

DriveLLaVA: Human-Level Behavior Decisions via Vision Language Model.

Human-level driving is the ultimate goal of autonomous driving. As the top-level decision-making aspect of autonomous driving, behavior decision establishes short-term driving behavior strategies by evaluating road structures, adhering to traffic rules, and analyzing the intentions of other traffic participants. Existing behavior decisions are primarily implemented based on rule-based methods, exhibiting insufficient generalization capabilities when faced with new and unseen driving scenarios. In this paper, we propose a novel behavior decision method that leverages the inherent generalization and commonsense reasoning abilities of visual language models (VLMs) to learn and simulate the behavior decision process in human driving. We constructed a novel instruction-following dataset containing a large number of image-text instructions paired with corresponding driving behavior labels, to support the learning of the Drive Large Language and Vision Assistant (DriveLLaVA) and enhance the transparency and interpretability of the entire decision process. DriveLLaVA is fine-tuned on this dataset using the Low-Rank Adaptation (LoRA) approach, which efficiently optimizes the model parameter count and significantly reduces training costs. We conducted extensive experiments on a large-scale instruction-following dataset, and compared with state-of-the-art methods, DriveLLaVA demonstrated excellent behavior decision performance. DriveLLaVA is capable of handling various complex driving scenarios, showing strong robustness and generalization abilities.

Feedback and Financial Incentives for Reducing Cell Phone Use While Driving: A Randomized Clinical Trial.

Importance: Handheld phone use while driving is a major factor in vehicle crashes. Scalable interventions are needed to encourage drivers not to use their phones. Objective: To test whether interventions involving social comparison feedback and/or financial incentives can reduce drivers' handheld phone use. Design, Setting, and Participants: In a randomized clinical trial, interventions were administered nationwide in the US via a mobile application in the context of a usage-based insurance program (Snapshot Mobile application). Customers were eligible to be invited to participate in the study if enrolled in the usage-based insurance program for 30 to 70 days. The study was conducted from May 13 to June 30, 2019. Analysis was completed December 22, 2023. Interventions: Participants were randomly assigned to 1 of 6 trial arms for a 7-week intervention period: (1) control; (2) feedback, with weekly push notification about their handheld phone use compared with that of similar others; (3) standard incentive, with a maximum $50 award at the end of the intervention based on how their handheld phone use compared with similar others; (4) standard incentive plus feedback, combining interventions of arms 2 and 3; (5) reframed incentive plus feedback, with a maximum $7.15 award each week, framed as participant's to lose; and (6) doubled reframed incentive plus feedback, a maximum $14.29 weekly loss-framed award. Main Outcome and Measure: Proportion of drive time engaged in handheld phone use in seconds per hour (s/h) of driving. Analyses were conducted with the intention-to-treat approach. Results: Of 17 663 customers invited by email to participate, 2109 opted in and were randomized. A total of 2020 drivers finished the intervention period (68.0% female; median age, 30 [IQR, 25-39] years). Median baseline handheld phone use was 216 (IQR, 72-480) s/h. Relative to control, feedback and standard incentive participants did not reduce their handheld phone use. Standard incentive plus feedback participants reduced their use by -38 (95% CI, -69 to -8) s/h (P = .045); reframed incentive plus feedback participants reduced their use by -56 (95% CI, -87 to -26) s/h (P < .001); and doubled reframed incentive plus feedback participants reduced their use by -42 s/h (95% CI, -72 to -13 s/h; P = .007). The 5 active treatment arms did not differ significantly from each other. Conclusions and Relevance: In this randomized clinical trial, providing social comparison feedback plus incentives reduced handheld phone use while individuals were driving. Trial Registration: ClinicalTrials.gov Identifier: NCT03833219.

Multifactorial assessment of braking time predictors in a driving simulator among older adults according to gender.

CONTEXT: Vehicle driving depends on the integration of motor, visual, and cognitive skills to respond appropriately to different situations that occur in traffic. OBJECTIVES: To analyze a model of performance predictor for braking time in the driving simulator, using a battery of tests divided by gender. METHODS: Selected were 100 male drivers with a mean age of 72.6 ± 5.7 years. Sociodemographic variables, braking time in the driving simulator, and motor, visual, and cognitive skills were evaluated. RESULTS: Comparing genders, men were older than women (p = 0.002) and had longer driving times (p = 0.001). Men had more strength in hand grip (p ≤ 0.001). In the linear regression analysis, the model explained 68 % of the braking time in men and 50.8 % in women. In the stepwise multiple linear regression analysis, the variable that remained in the model was the strength of the right plantar flexors, which explained 13 % of the braking time in women and men, and the cognitive variables explained 38.9 %. CONCLUSION: Sociodemographic, motor, visual, and cognitive variables, explained a substantial portion of the variability in braking time for both older women and men, the specific variables driving this performance differed between the sexes. For older women, factors such as muscle strength emerged as critical determinants of braking ability, highlighting the importance of physical health in maintaining driving skills. On the other hand, cognitive conditions emerged as the primary predictor of braking performance in older men, underscoring the role of mental acuity and decision-making processes in safe driving.

Influences of Vehicle Communication on Human Driving Reactions: A Simulator Study on Reaction Times and Behavior for Forensic Accident Analysis.

Cooperative intelligent transport systems (C-ITSs) are mass-produced and sold in Europe, promising enhanced safety and comfort. Direct vehicle communication, known as vehicle-to-everything (V2X) communication, is crucial in this context. Drivers receive warnings about potential hazards by exchanging vehicle status and environmental data with other communication-enabled vehicles. However, the impact of these warnings on drivers and their inclusion in accident reconstruction remains uncertain. Unlike sensor-based warnings, V2X warnings may not provide a visible reason for the alert, potentially affecting reaction times and behavior. In this work, a simulator study on V2X warnings was conducted with 32 participants to generate findings on reaction times and behavior for accident reconstruction in connection with these systems. Two scenarios from the Car-2-Car Communication Consortium were implemented: "Stationary Vehicle Warning-Broken-Down Vehicle" and "Dangerous Situation-Electronic Emergency Brake Lights". Volkswagen's warning concept was utilized, as they are the sole provider of cooperative vehicles in Europe. Results show that V2X warnings without visible reasons did not negatively impact reaction times or behavior, with average reaction times between 0.58 s (steering) and 0.69 s (braking). No significant distraction or search for warning reasons was observed. However, additional information in the warnings caused confusion and was seldom noticed by subjects. In this study, participants responded correctly and appropriately to the shown false-positive warnings. A wrong reaction triggering an accident is possible but unlikely. Overall, V2X warnings showed no negative impacts compared with sensor-based systems. This means that there are no differences in accident reconstruction regarding the source of the warning (sensors or communication). However, it is important that it is known that there was a warning, which is why the occurrence of V2X warnings should also be saved in the EDR in the future.

[Mobility, driving, and functional competence in older people-selected results from the Longitudinal Urban Cohort Ageing Study (LUCAS)]. / Mobilität, Autofahren und funktionale Kompetenz bei älteren Menschen ­ Ausgewählte Ergebnisse einer Längsschnittstudie zur Alterung urbaner Kohorten (LUCAS).

Mobility is crucial for independent living in old age. Older people with reduced physical ability (frailty) begin to limit their personal range of activities to their immediate living environment and ultimately to their immediate home. Diseases of the musculoskeletal system as well as neurological, psychological, cognitive, sensory, and circulatory disorders can limit functional competence (ability to live independently).In the Longitudinal Urban Cohort Ageing Study (LUCAS), from which selected results are reported in this article, participants were categorized into different functional classes (Robust, postRobust, preFrail, Frail) using the LUCAS functional index. The results show that losses in functional competence were associated with impaired mobility and reduced car driving. Impaired mobility led to restricted radius of action.The aim of healthcare in old age is to preserve independence and quality of life as long as possible. Car driving is an important part of older peoples' activities of daily living. Therefore, primary care physicians should address car driving regularly because preventive measures to strengthen functional health also strengthen car driving ability in older persons.

Factors Influencing Participation and Engagement in a Teen Safe Driving Intervention: A Qualitative Study.

(1) Background: Few teen driving safety programs focus on increasing parental engagement with high-risk teen drivers, specifically those with a traffic violation. This study explored parents'/guardians' ('parents') experiences with a teen driving safety program, ProjectDRIVE, including facilitators and barriers to program engagement. (2) Methods: We conducted virtual, semi-structured interviews with parents who completed ProjectDRIVE, which included in-vehicle driving feedback technology and individualized virtual training with parents on effective parent-teen communication. (3) Results: Twenty interviews (with 17 females and three males) were transcribed verbatim and independently coded by three coders using systematic, open, and focused coding. Three major themes were identified: factors influencing a parent's initial decision to participate, factors influencing continued engagement, and perceived benefits of participation. The decision to participate was influenced by these subthemes: parental motivation to help their teen, perceived program usefulness, program endorsement, program incentives, parents' busy schedules, and lack of access to a car/internet. Subthemes impacting continued engagement included enhanced communication skills, teen willingness to engage, strong parental engagement, and teens' other priorities. Perceived benefits included greater self-efficacy in communication, improved communication patterns and frequency, and enhanced parent-teen relationships. (4) Conclusions: These findings may set the foundation for developing and implementing future court-ordered parent-based teen safe driving programs for teens with traffic citations.

Prevalence and Profiles of Risky Driving Behavior Among US Teenagers.

This survey study updates prevalence data on risky driving behaviors in a nationally representative sample of young people in the US and characterizes profiles of drivers according to the types of risky behaviors they engaged in.

Audiovisual presentation of variable message signs improves message processing in distracted drivers during partially automated driving.

INTRODUCTION: Highly automated driving is expected to reduce the accident risk occurrence by human errors, but it can also increase driver distraction. Previous evidence shows that auditory signals can help drivers take over in critical situations. However, it is still uncertain whether the potential benefit of verbal auditory signals could be generalized to driving situations where drivers are visually and auditorily distracted. METHOD: Our first objective was to compare the effectiveness of complementary audio messages (audio + visual condition) and visual only (visual condition) variable message signs (VMS) messages. The second objective was to explore the potential use of oral messages with traffic information to help highly-automated vehicle drivers identify critical situations. Eye-tracking data were also registered. Twenty-four volunteers participated in a driving simulator study, completing two tasks: (a) a TV series task, where they had to pay attention to an episode of a TV series while traveling along the route; and (b) a VMS task, where they had to recover the manual control of the car if the VMS message was a 'critical message.' RESULTS: General results showed that, when the audio was available, the participants: (a) had a higher ability to discriminate the VMS messages, (b) were less conservative, (c) responded earlier, and (d) their pattern of fixations was more efficient. A complementary analysis showed that the counterbalance order was a moderating factor for the discrimination ability and the response distance measures. This evidence suggests a potential learning effect, not cancelled by counterbalancing the order of the conditions. CONCLUSION: The processing of traffic messages may improve when provided as oral and visual messages. PRACTICAL APPLICATIONS: These results would be of special interest for engineers designing highly automated cars, considering that the design of automated systems must ensure that the driver's attention is sufficient to take over control.

Assessing the determinants of crash propensity using structural equation modeling: Role of distractions caused by fellow drivers.

INTRODUCTION: Aggressive behavior of drivers is a source of crashes and high injury severity. Aggressive drivers are part of the driving environment, however, excessive aggressive driving by fellow drivers may take the attention of the recipient drivers away from the road resulting in distracted driving. Such external distractions caused by the aggressive and discourteous behavior of other road users have received limited attention. These distractions caused by fellow drivers (DFDs) may agitate recipient drivers and ultimately increase crash propensity. Aggressive driving behaviors are quite common in South Asia and, thus, it is necessary to determine their contribution to distractions and crash propensity. METHOD: Our study aimed to evaluate the effects of DFDs using primary data collected through a survey conducted in Lahore, Pakistan. A total of 801 complete responses were obtained. Various hypotheses were defined to explore the associations between the latent factors such as DFDs, anxiety/stress (AS), anxiety-based performance deficits (APD), hostile behavior (HB), acceptability of vehicle-related distractions (AVRD), and crash propensity (CP). Structural Equation Modeling (SEM) was employed as a multivariate statistical technique to test these hypotheses. RESULTS: The results supported the hypothesis that DFDs lead to AS among recipient drivers. DFDs and AS were further found to have positive associations with APDs. Whereas, there was a significant negative association between DFD, AS, and AVRD. As hypothesized, DFD and AS had positive associations with CP, indicating that distractions caused by aggressive behaviors leads to stress and consequently enhances crash propensity. PRACTICAL APPLICATIONS: The results of this study provide a statistically sound foundation for further exploration of the distractions caused by the aggressive behaviors of fellow drivers. Further, the results of this study can be utilized by the relevant authorities to alter aggressive driving behaviors and reduce DFDs.

Impact of cognitive impairment on driving behaviour and route choices of older drivers: a real-world driving study.

Maintaining driving independence is important for older adults. However, cognitive decline, a common issue in older populations, can impair older adults' driving abilities and overall safety on the roads. This study explores how cognitive impairment influences driving patterns and driving choices among older adults. We analyzed real-world driving patterns of 246 older adults using GPS dataloggers. Our sample included 230 cognitively normal older adults (CN; Clinical Dementia Rating R [CDR] = 0) and 16 older adults with incident cognitive impairment (ICI; CDR = 0.5). The CN group had an average age of 68.2 years, with 46% females and an average of 16.5 years of education, while the ICI group's average age was 69.2 years, with 36% females and an average of 16.0 years of education. We employed spatial clustering and hashing algorithms to evaluate driving behaviours. Significant differences emerged: The ICI group used fewer distinct routes to their most common destination. These differences can be leveraged to develop driving as a digital biomarker for the early detection and continuous monitoring of cognitive impairment.

Driving Cognitive Alertness Detecting Using Evoked Multimodal Physiological Signals Based on Uncertain Self-Supervised Learning.

Multimodal physiological signals play a pivotal role in drivers' perception of work stress. However, the scarcity of labels and the multitude of modalities render the utilization of physiological signals for driving cognitive alertness detection challenging. We thus propose a multimodal physiological signal detection model based on self-supervised learning. First, in order to mine the intrinsic information of data and enable data to highlight effective information, we introduce a multiscale entropy (MSE) evoked attention mechanism. Secondly, the multimodal patches undergo processing through a novel cascaded attention mechanism. This attention mechanism is rooted in patch-level interactions within each modality, progressively integrating and interacting with other modalities in a cascading manner, thereby mitigating computational complexity. Moreover, a multimodal uncertainty-aware module is devised to effectively cope with intricate variations in the data. This module enhances its generalization ability through the incorporation of uncertain resampling. Experiments were conducted on the DriveDB dataset and the CogPilot dataset with both the linear probing and the fine-tuning evaluation protocols. Experimental results in subject-dependent setting show that our model significantly outperforms previous competitive baselines. In the linear probing evaluation, our model achieves on average 6.26%, 6.64%, and 7.75% improvements in Accuracy (Acc), Recall (Rec), and F1 Score. It also outperforms other models by 7.96% in Acc, 9.13% in Rec, and 9.2% in F1 using the fine-tuning evaluation. Furthermore, our model also demonstrates robust performance in subject-independent setting.

Hazard Perception in Visually Impaired Drivers Who Use Bioptic Telescopes.

Purpose: Bioptic telescopic spectacles can allow individuals with central vision impairment to obtain or maintain driving privileges. The purpose of this study was to (1) compare hazard perception ability among bioptic drivers and traditionally licensed controls, (2) assess the impact of bioptic telescopic spectacles on hazard perception in drivers with vision impairment, and (3) analyze the relationships among vision and hazard detection in bioptic drivers. Methods: Visual acuity, contrast sensitivity, and visual field were measured for each participant. All drivers completed the Driving Habits Questionnaire. Hazard perception testing was conducted using commercially available first-person video driving clips. Subjects signaled when they could first identify a traffic hazard requiring a change of speed or direction. Bioptic drivers were tested with and without their bioptic telescopes in alternating blocks. Hazard detection times for each clip were converted to z-scores, converted back to seconds using the average response time across all videos, and then compared among conditions. Results: Twenty-one bioptic drivers and 21 normally sighted controls participated in the study. The hazard response time of bioptic drivers was improved when able to use the telescope (5.4 ± 1.4 seconds vs 6.3 ± 1.8 seconds without telescope); however, it remained significantly longer than for controls (4.0 ± 1.4 seconds). Poorer visual acuity, contrast sensitivity, and superior visual field sensitivity loss were related to longer hazard response times. Conclusions: Drivers with central vision loss had improved hazard response times with the use of bioptic telescopic spectacles, although their responses were still slower than normally sighted control drivers. Translational Relevance: The use of a bioptic telescope by licensed, visually impaired drivers improves their hazard detection speed on a video-based task, lending support to their use on the road.

Effects of multimodal explanations for autonomous driving on driving performance, cognitive load, expertise, confidence, and trust.

Advances in autonomous driving provide an opportunity for AI-assisted driving instruction that directly addresses the critical need for human driving improvement. How should an AI instructor convey information to promote learning? In a pre-post experiment (n = 41), we tested the impact of an AI Coach's explanatory communications modeled after performance driving expert instructions. Participants were divided into four (4) groups to assess two (2) dimensions of the AI coach's explanations: information type ('what' and 'why'-type explanations) and presentation modality (auditory and visual). We compare how different explanatory techniques impact driving performance, cognitive load, confidence, expertise, and trust via observational learning. Through interview, we delineate participant learning processes. Results show AI coaching can effectively teach performance driving skills to novices. We find the type and modality of information influences performance outcomes. Differences in how successfully participants learned are attributed to how information directs attention, mitigates uncertainty, and influences overload experienced by participants. Results suggest efficient, modality-appropriate explanations should be opted for when designing effective HMI communications that can instruct without overwhelming. Further, results support the need to align communications with human learning and cognitive processes. We provide eight design implications for future autonomous vehicle HMI and AI coach design.

A panoramic driving perception fusion algorithm based on multi-task learning.

With the rapid development of intelligent connected vehicles, there is an increasing demand for hardware facilities and onboard systems of driver assistance systems. Currently, most vehicles are constrained by the hardware resources of onboard systems, which mainly process single-task and single-sensor data. This poses a significant challenge in achieving complex panoramic driving perception technology. While the panoramic driving perception algorithm YOLOP has achieved outstanding performance in multi-task processing, it suffers from poor adaptability of feature map pooling operations and loss of details during downsampling. To address these issues, this paper proposes a panoramic driving perception fusion algorithm based on multi-task learning. The model training involves the introduction of different loss functions and a series of processing steps for lidar point cloud data. Subsequently, the perception information from lidar and vision sensors is fused to achieve synchronized processing of multi-task and multi-sensor data, thereby effectively improving the performance and reliability of the panoramic driving perception system. To evaluate the performance of the proposed algorithm in multi-task processing, the BDD100K dataset is used. The results demonstrate that, compared to the YOLOP model, the multi-task learning network performs better in lane detection, drivable area detection, and vehicle detection tasks. Specifically, the lane detection accuracy improves by 11.6%, the mean Intersection over Union (mIoU) for drivable area detection increases by 2.1%, and the mean Average Precision at 50% IoU (mAP50) for vehicle detection improves by 3.7%.

Road to serenity: Individual variations in the efficacy of unobtrusive respiratory guidance for driving stress regulation.

Stress impacts driving-related cognitive functions like attention and decision-making, and may arise in automated vehicles due to non-driving tasks. Unobtrusive relaxation techniques are needed to regulate stress without distracting from driving. Tactile wearables have shown efficacy in stress regulation through respiratory guidance, but individual variations may affect their efficacy. This study assessed slow-breathing tactile guidance under different stress levels on 85 participants. Physiological, behavioral and subjective data were collected. The influence of individual variations (e.g., driving habits and behavior, personality) using logistic regression analysis was explored. Participants could follow the guidance and adjust breathing while driving, but subjective efficacy depended on individual variations linked to different efficiency in using the technique, in relation with its attentional cost. An influence of factors linked to the evaluation of context criticality was also found. The results suggest that considering individual and contextual variations is crucial in designing and using such techniques in demanding driving contexts. In this line some design recommendations and insights for further studies are provided.

Perceived impacts of stressful events on train driver performance.

Acute stress exposure can significantly impact a train driver's capacity to maintain safe train operations. However, research examining how train drivers perceive the impacts of acute stressors is limited. This study investigated train driver perceptions regarding performance impacts of stressful events and potential strategies for reducing negative impacts. 71 Australian train drivers were presented with three stressful event scenarios via an online survey and asked to rate the impacts on driving performance. Results showed that participants perceived that stress would enhance performance, but that impacts differed depending on the event type. The findings suggest that train drivers may not be subjectively aware of negative impacts of acute stress, which has important practical implications for risk management following an incident. Qualitative results revealed the most frequently reported stress impact related to cognition. Practical implications and future research directions to prevent and manage stressful event exposure are discussed.

"You just have to be careful how you do it": A qualitative study of the Healthwise decision aid for older drivers.

OBJECTIVE: Decisions about driving retirement are difficult for older adults, their families, and health care providers. A large randomized trial found that an existing online Healthwise decision aid decreased decision conflict and increased knowledge about driving decisions. This study sought to discover how, when, and where the tool might be most effective for older drivers, their family members, and their health care providers. METHODS: We used one-on-one, semistructured interviews (June-December 2023) to explore perspectives on the content of the Healthwise online driving decision aid and its potential use. Participants were health care providers or subject matter experts in older driver research or policy. Transcribed interviews were coded and analyzed with a team-based approach to identify emerging themes. RESULTS: Across interviews (16 health care providers; 15 experts), emerging themes related to considerations (barriers, benefits, and settings for use) that were (1) individual or interpersonal or (2) institutional or cultural, as well as feedback on (3) decision aid content and structure. Findings included concerns over agism and damaging provider-patient relationships, along with identified benefits of integrating tools into electronic health records and a need for consolidated, easy-to-access resources for both providers and patients. CONCLUSION: Attention to individual, interpersonal, institutional, and cultural factors may enhance the use and dissemination of an online decision aid about driving, as well as its effectiveness in decision making. Future work should include views of additional stakeholders and studies on implementation of decision aids into real-world settings.

Evaluation of harmful drinking among professional drivers by direct ethanol biomarkers and its relation with psychological distress.

OBJECTIVE: This study aimed to evaluate the alcohol consumption among professional truck and bus drivers using direct ethanol biomarkers, and to explore its relationship with anxiety, depression, and stress. METHODS: The assessment of potential harmful drinking was conducted through the measurement of direct biomarkers: phosphatidylethanol (PEth), ethyl glucuronide (EtG), and ethyl sulfate (EtS), using dried blood spots (DBS). Additionally, self-reported data from the Alcohol Use Disorders Identification Test (AUDIT-C) were used. Emotional states, including depression, anxiety, and stress, were evaluated using the Depression, Anxiety, and Stress Scale (DASS-21). RESULTS: A total of 97 drivers participated in the study, with the majority being male (96%) and identified as truck drivers (75.3%). Among them, 43.3% reported working more than 10 h daily. The majority of volunteers exhibited normal levels of stress (81.4%), anxiety (83%), and depression (86.6%). According to the AUDIT-C assessment, 30.9% were categorized as having a moderate risk, while 11.3% were deemed to be at high risk for harmful alcohol consumption behavior. Ethyl glucuronide (EtG) and ethyl sulfate (EtS) levels, indicating recent ethanol consumption, were detected in 14.4% of the drivers. In contrast, the long half-life metabolite PEth (16:0-18:1) was present in 88.7% of the volunteers. A moderate correlation (rs = 0.45, p < .01) was observed between PEth levels and AUDIT-C scores. The Receiver Operating Characteristic (ROC) curve, utilizing a PEth threshold of ≥ 59.0 ng ml-1, displayed 78% sensitivity and 73% specificity in effectively distinguishing high risk for alcohol intake. Notably, no significant associations were found between alcohol consumption and levels of stress, depression, and anxiety. CONCLUSIONS: The study findings indicate a noteworthy proportion of drivers engaging in regular alcohol consumption alongside a demanding workload. Notably, PEth measurements highlighted an underreporting within the AUDIT-C self-reports. These results lend robust support for the utilization of biomarkers in assessing alcohol consumption patterns among drivers.

Attentional warnings caused by driver monitoring systems: How often do they appear and how well are they understood?

The present study investigated the effects of a driver monitoring system that triggers attention warnings in case distraction is detected. Based on the EuroNCAP protocol, distraction could either be long glances away from the forward roadway (≥3s) or visual attention time sharing (>10 cumulative seconds within a 30 s time interval). In a series of manual driving simulator drives, 30 participants completed both driving related tasks (e.g., changing multiple lanes in dense traffic) and non-driving related tasks (e.g., infotainment operations). Results of warning frequencies revealed that visual attention time sharing warnings occurred more frequently than long distraction warnings. Moreover, there was a large number of attention warnings during driving related tasks. Results also revealed that participants' mental models tended to be less accurate when it came to understanding of the visual attention time sharing warnings as compared to the long distraction warnings, which were understood more accurately. Based on these observations, the work discusses the applicability and design of driver monitoring warnings.